{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:27:22Z","timestamp":1758126442317,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030670696"},{"type":"electronic","value":"9783030670702"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-67070-2_10","type":"book-chapter","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T20:02:51Z","timestamp":1611950571000},"page":"171-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["EEDNet: Enhanced Encoder-Decoder Network for AutoISP"],"prefix":"10.1007","author":[{"given":"Yu","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Leng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"10_CR1","unstructured":"Abdelhamed, A., et al.: Ntire 2020 challenge on real image denoising: Dataset, methods and results. arXiv preprint arXiv:2005.04117 (2020)"},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Araujo, A., Norris, W., Sim, J.: Computing receptive fields of convolutional neural networks. Distill (2019). https:\/\/doi.org\/10.23915\/distill.00021, https:\/\/distill.pub\/2019\/computing-receptive-fields","DOI":"10.23915\/distill.00021"},{"key":"10_CR3","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"issue":"4","key":"10_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., Chua, T.S.: Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5659\u20135667 (2017)","DOI":"10.1109\/CVPR.2017.667"},{"key":"10_CR6","unstructured":"Fan, Y., Yu, J., Liu, D., Huang, T.S.: Scale-wise convolution for image restoration (2019)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Ignatov, A., Patel, J., Timofte, R.: Rendering natural camera bokeh effect with deep learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2020)","DOI":"10.1109\/CVPRW50498.2020.00217"},{"key":"10_CR8","unstructured":"Ignatov, A., Timofte, R., et al.: AIM 2020 challenge on learned image signal processing pipeline. In: European Conference on Computer Vision Workshops (2020)"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Ignatov, A., Van Gool, L., Timofte, R.: Replacing mobile camera isp with a single deep learning model. arXiv preprint arXiv:2002.05509 (2020)","DOI":"10.1109\/CVPRW50498.2020.00276"},{"key":"10_CR10","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"10_CR11","unstructured":"Liang, Z., Cai, J., Cao, Z., Zhang, L.: Cameranet: A two-stage framework for effective camera isp learning (2019)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"10_CR14","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/cgf.13340","volume":"37","author":"D Marnerides","year":"2017","unstructured":"Marnerides, D., Bashford-Rogers, T., Hatchett, J., Debattista, K.: Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content. Comput. Graph. Forum 37(2), 37\u201349 (2017)","journal-title":"Comput. Graph. Forum"},{"key":"10_CR15","unstructured":"Nah, S., Son, S., Timofte, R., Lee, K.M.: Ntire 2020 challenge on image and video deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Purohit, K., Suin, M., Kandula, P., Ambasamudram, R.: Depth-guided dense dynamic filtering network for bokeh effect rendering. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW). pp. 3417\u20133426 (2019)","DOI":"10.1109\/ICCVW.2019.00424"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Ratnasingam, S.: Deep camera: A fully convolutional neural network for image signal processing. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00480"},{"key":"10_CR18","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems. pp. 91\u201399 (2015)"},{"key":"10_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"10_CR20","doi-asserted-by":"publisher","first-page":"4160","DOI":"10.1109\/TIP.2018.2834737","volume":"27","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Yu, Y., Wang, W.: Moir\u00e9 photo restoration using multiresolution convolutional neural networks. IEEE Trans. Image Process. 27(8), 4160\u20134172 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00853"},{"key":"10_CR22","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"10_CR24","doi-asserted-by":"publisher","first-page":"4308","DOI":"10.1109\/TIP.2020.2971346","volume":"29","author":"Q Yan","year":"2020","unstructured":"Yan, Q., et al.: Deep hdr imaging via a non-local network. IEEE Trans. Image Process. 29, 4308\u20134322 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Yuan, S., et al.: Aim 2019 challenge on image demoireing: Methods and results. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW). pp. 3534\u20133545 (2019)","DOI":"10.1109\/ICCVW.2019.00438"},{"issue":"7","key":"10_CR26","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"10_CR27","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67070-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T23:10:59Z","timestamp":1738105859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67070-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030670696","9783030670702"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67070-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}